Mango Fruit Yield and Critical Quality Parameters Respond to Foliar and Soil Applications of Zinc and Boron
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mango (Mangifera indica L.), the sixth most important fruit crop worldwide, is likely at risk under a climate change scenario of accelerated soil organic matter mineralization and constrained plant nutrient supplies such as zinc (Zn) and boron (B). We identified the optimum nutrient formulation and application method to possibly rectify nutrient deficits in mango plants grown in one of the warmest and driest regions—Multan, Pakistan. We evaluated the yield and physiological (quality) responses of 20-year-old mango trees to seven treatments of foliar and soil applications of Zn and B. Combined soil application of B and Zn resulted in optimum increases in leaf mineral B and Zn and fruit-set, retention, yield, pulp recovery and total soluble solids at ripening (p = 0.021), while reducing titratable acidity and early fruit shedding (p = 0.034). Additionally, this treatment improved fruit quality (taste, flavour, texture, aroma, acceptability; p ≤ 0.05). Yield was found to be correlated with retention percentage (P ≤ 0.001; R2 = 0.91), which was in turn related to fruit-set number panicle−1 (P = 0.039; R2 = 0.61). Therefore, we suggest that combined soil application of B and Zn mitigates leaf mineral deficiencies and improves the yield and quality of mango more efficiently than other individual or combined foliar or soil treatments used in this study.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it